Method and apparatus for biometric analysis using EEG and EMG signals

ABSTRACT

Biometric assessment is performed by use of electromyography (EMG) signals detected from muscles at several locations on the hand/or other part of the body subject to fine motor control. In addition, electroencephalography (EEG), signals detect other biomarkers. The EMG and EEG signals are sensed, synchronized and registered. The signals are converted into digital data and are stored and processed for use in performing the biometric assessment.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/588,675 filed Oct. 23, 2009, which claims the benefit ofU.S. Provisional Application No. 61/108,603, filed Oct. 27, 2008, andU.S. patent application Ser. No. 11/640,954, filed Dec. 19, 2006,published as U.S. Published Application No. 2007-0140562, and whichclaims the benefit of U.S. Provisional Application No. 60/751,905, filedDec. 21, 2005. The aforementioned provisional applications' disclosuresare incorporated herein by reference in their entirety.

BACKGROUND

1. Field

This disclosure relates to the use of signals obtained from a limb(hand, and/or forearm, etc.) or other portion of the body subject tofine motor control, in medical diagnostics and biometrics, and to theuse of electromyography (EMG) signals for medical, biometrics andrelated uses. The EMG is thereby used for the purposes of biometricassessment.

2. Background

The prior art is rich in various systems and methods in the area ofdigital handwriting, as well as various systems and methods relating touseful endeavors. In general, most existing systems and methods provideconcrete functions, which have a defined response to a defined stimulus.Such systems, while embodying the “wisdom” of the designer, have aparticular shortcoming in that their capabilities are limited.

According to the research that was conducted in Haifa University,handwriting problems are also clues to developmental, neurological,behavioral, or medical conditions such as ADHD and Parkinson's disease.Disturbances in handwriting legibility and speed (known as dysgraphia)are problematic for about 10-30% of elementary school-aged children.Many adults who suffer from neuromuscular pathologies of different types(e.g., Parkinson's disease, multiple sclerosis, Alzheimer's disease)also experience progressive deterioration of the quality of theirhandwriting. Dysgraphic writing has a variety of academic, emotional andsocial consequences. The comprehensive and detailed characterization ofdysgraphia has diagnostic and treatment value, helping clinicians todifferentiate between levels of motor involvement, to evaluate theeffectiveness of medication and to achieve better techniques forhandwriting remediation.

SUMMARY

Biometric assessment is performed by use of electrodes to senseelectromyography (EMG) signals used for fine motor control by thesubject. In one configuration, the signals are obtained from electrodesapplied to the hand and/or forearm of a subject by use of a glove orsimilar garment. The sensed EMG signals are analyzed to detect patternsin the EMG signals.

The invention was based upon our discovery of biometric signals that canbe analyzed to provide biomarkers for one or more disorders. Thebiometric signals can be collected and analyzed to provide fourbiomarkers, including two biomarkers from EMG signals and two from EEGsignals. The biomarkers are based on identifying patterns of biometricsignals in healthy subjects that are different from correspondingpatterns in patients with disorders. For the particular disorder ofParkinson's Disease (PD), we found that (1) PD patients had EMG signalswith evenly distributed intensities without distinctive short bursts,(2) PD patients had low correlations for the same channels, (3) PDpatients demonstrated a growth of EEG correlations in cortex areasactivated during handwriting, and (4) PD patients had EEG signals withcorrelations which endured over longer time intervals.

Using these discoveries we found, in general, that healthy subjects haveone or more biometric signals which have defined patterns orcharacteristics during predetermined handwriting samples of one or morecharacters. However, patients with disorders such as PD have differentbiometric signals for the same handwriting samples. In other words, thepatient with a disorder may be identified by having patterns ofbiometric signals with characteristics that do not correspond to thepattern of characteristics of the known healthy subjects. The resultsdepend upon taking patient biometric signals over multiple, timedintervals of handwriting samples and time-stamping biometric signalsduring each interval of handwriting sample.

The invention may be embodied in one or more methods or one or moreinstruments or devices. Each embodied method or instrument or device maymay detect one or more or all of the biomarkers that characterize adisorder. Each biomarker is, to some degree, a difference in between apattern of biometric signals found in healthy subjects compared to adifferent pattern of biometric signals found in patients with PD orother disorders.

BRIEF DESCRIPTION OF THE DRAWINGS

The features, nature, and advantages of the present technique willbecome more apparent from the detailed description set forth below whentaken in conjunction with the drawings in which like referencecharacters identify corresponding items throughout and wherein:

FIG. 1 is a schematic block diagram a circuit used to senseelectromyographic (EMG) data and provide an output based on the senseddata.

FIG. 2 is a diagram showing details of one of the sensor circuits ofFIG. 1.

FIG. 3 is a diagram showing an example of a user interface garment.

FIG. 4 is a diagram depicting a functional implementation of a system torecord a handwriting activity.

FIG. 5 is a diagram depicting a functional implementation of a system togenerate a biometric assessment based on controlled hand movement.

FIG. 6 is a high level schematic view of the laboratory set up used toidentify biomarkers.

FIG. 7A is a high level schematic view of one embodiment of theinvention.

FIG. 7B is a high level schematic view of an EEG only embodiment of theinvention.

FIG. 8 is a view of the gloves and channels used for the laboratory setup and the embodiment of the invention shown in FIGS. 6 and 7.

FIG. 9 is a graphic representation of a template of EMG intensity of EMGin two channels for control healthy subject.

FIG. 10 is a graphic representation of a template of EMG intensity ofEMG in two channels for a PD patient.

FIG. 11 is a graphic representation of timed behavior of Pearsoncorrelation coefficient (center bar) for EMG signals recorded in twodifferent channels (EMG1 and EMG2) for a healthy control subject.

FIG. 12 is a graphic representation of timed behavior of Pearsoncorrelation coefficient (center bar) for EMG signals recorded in twodifferent channels (EMG1 and EMG2) for a PD patient.

FIG. 13 is a schematic representation of EEG channels locations.

FIG. 14 is a graphic representation showing the time dependence ofPearson correlation coefficients for a healthy control between channel13 and all other channels.

FIG. 15 is a graphic representation showing Pearson correlationcoefficients Pmean of channel 13 (C3) with all channels duringhandwriting (healthy control)

FIG. 16 is a graphic representation showing Pearson correlationcoefficients Pmean of channel 15 (C4) with all channels duringhandwriting (healthy control).

FIG. 17 is a graphic representation showing Pearson correlationcoefficients Pmean of channel 23 (P7) with all channels duringhandwriting (healthy control).

FIG. 18 is a graphic representation showing Pearson correlationcoefficients Pmean of channel 27 (P8) with all channels duringhandwriting (healthy control).

FIG. 19 is a graphic representation showing healthy control cortexactivity regions using the International naming convention.

FIG. 20 is a graphic representation showing healthy control cortexactivity regions using a vendor specific naming

FIG. 21 is a graphic representation showing correlation coefficientsP_(mean) computed for control healthy subject.

FIG. 22 is a graphic representation showing a PD patient, stage I, onmedication with high correlations between all cortex channels.

FIG. 23 is a graphic representation showing a PD patient, stage III, offmedication with high correlations between all cortex channels.

FIG. 24 is a graphic representation showing correlation coefficients ofEEG signal (channel 13) of healthy control subject as a function of twotime intervals.

FIG. 25 is a graphic representation showing correlation coefficient ofEEG signal (channel 13) of PD patient as a function of two timeintervals.

DETAILED DESCRIPTION

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any embodiment described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. The word “example” is used hereinto mean “a non-limiting example.” Each example provided herein is anillustration of merely one embodiment; many others may exist, and noprovided example should be construed as limiting an otherwise broadercategory.

Overview

Handwriting activity is an example of fine motor control, and ischaracterized by sophisticated, controlled hand movement. Such handmotion activity resulting in controlled hand movement may bereconstructed in a digital format by applying computer algorithmsdirectly to electromyography (EMG) signals. Pattern classification andrecognition algorithms are used in conjunction with pattern recognitiontechniques. In the case of handwriting reconstruction, a recording isperformed through a data processing routine, a handwritingreconstruction routine, and a routine that generates machine editabletext.

Handwriting is given as an example of controlled hand movement; howeverother forms of fine motor control can be exhibited which arecharacteristic of sophisticated, controlled hand motion or sophisticatedcontrolled fine motor control. In addition to fine motor control, finemotor control in combination with movement or holding of light ormoderate weight (e.g., <5 kg) can be used to sense neurologicalconditions. Handwriting is a sophisticated form of controlled handmovement and is notable because it involves three factors:

-   memory;-   knowledge; and-   dexterity.

The last factor, dexterity, is the most familiar characteristic; howeverthe other two factors are significant in handwriting activity and assuch are also reflected in EMG patterns derived from sensinghandwriting. Memory and knowledge are significant because a neurologicalor other biological process that affects the types of memory andknowledge used to perform handwriting activity would be detectablethrough the EMG patterns. This combination of memory, knowledge anddexterity is analogous to the “clock drawing test” used for testing forAlzheimer's dementia and other forms of dementia; however thecombination of memory, knowledge and dexterity as expressed throughhandwriting is sensed as a part of controlled hand movement. Unlike the“clock drawing test”, the combination of memory, knowledge and dexterityas expressed through handwriting can be sensed through EMG. Thus, thehandwriting process represents a dynamic combination of conscious andsubconscious activities.

A biosensor that can read the biometric signals from the muscles of ahand, and/or a forearm, using electromyography (EMG) is used to provideEMG signals. The EMG signals are then analyzed. The analysis can be usedin medical diagnostics and biometrics, and in the use of EMG signals formedical, biometrics and related use, collectively referred to asbiometric assessment.

While it is possible to use the EMG signals for handwriting recognitionand, for the purposes of biometric assessment, the EMG signals areanalyzed for features which can be related to either known patternscharacteristic of a particular condition and/or for changes from one ormore readings from the individual taken at different times. Thissoftware will process and interpret the biometric signals for thepurposes of biometric assessment.

Effects of Biological Processes

One use for the biometric assessment is in the diagnosis of disease. Byway of example, motor dysfunction is believed to have an associationwith liver cirrhosis and mild forms of hepatic encephalopathy. Themechanisms and clinical appearance of motor impairment in patients withliver cirrhosis are not completely understood; however, encephalopathyis a well known disease that develops as a result of liver malfunction.Encephalopathy refers to brain and nervous system damage that resultfrom the liver's inability to metabolize toxins into harmlesssubstances. More generally, in the case of liver disease, toxins buildup in the body, causing various symptoms, one of which is difficultywith handwriting and other controlled hand movement. Fine motor controlwas studied in patients with advanced liver cirrhosis (excluding thosewith hepatic encephalopathy grade II) and 48 healthy controls using akinematic analysis of standardized handwriting tests, and parameters ofvelocity, the ability to coordinate and the level of automatization ofhandwriting movements were analyzed. There is also believed to be anassociation between impairment of handwriting and other controlled handmovement with clinical neuro-psychiatric symptoms. It is expected thatpatients will show a statistically significant reduction of movementpeak velocity in all controlled hand movement tasks as well as asubstantial increase of number of velocity inversions per stroke. Usinga z-score based assessment researchers have found impairments ofhandwriting in patients. The deterioration of handwriting was believedto be associated with clinical symptoms of motor dysfunction, such asbradykinesia, adi-adochokinesia, dysmetria of upper extremities and gaitataxia. This association suggests the application of kinematic analysisof handwriting for diagnostics of motor dysfunction in patients withmild forms of hepatic encephalopathy.

The analysis of EMG patterns, obtained from hand muscles duringcontrolled hand movement can detect cognitive impairment and motorimpairment much earlier than particular symptoms manifest themselves andwill be noticed by people. The controlled hand movement can be, by wayof non-limiting example, handwriting, such as writing alphanumericcharacters, but can be other forms of handwriting and other forms ofcontrolled hand movement. For the purposes of biometric assessment, itis not necessary to analyze handwriting per se, but rather the patternsof bio electricity, generated by neurons and muscles during some skilledhand movement.

EMG, as used for biometric assessment is useful in diagnostics ofvarious medical diseases in a human body. The diagnostics can relate todeficiencies, injuries, malfunctions, and diseases, including but notlimited to various forms of cancer, diabetes, genetic diseases, prenataltests including but not limited to tests for Down syndrome, HIV,Parkinson's disease, Alzheimer's disease, multiple scleroses, braininjuries and stroke, hepatic disease, blood system disease andmalfunctions, hormone system disease, nervous system diseases, centralnervous system (CNS) diseases, autonomic nervous system changes andcerebral disorders. Many algorithms that are currently used to study EMGpatterns in connection with hand and/or forearm movements can be alsoused to find associations with malfunctions of various organs. Livercirrhosis, hepatic encephalopathy, and many others can affect variousinternal organs, like brain, lungs, blood, liver, spleen, stomach, etc.There are also publications about ongoing research identifying theassociations between handwriting and heart diseases.

The disclosed use of EMG for biometric assessment can be use fordiagnosis and monitoring of various types of heart diseases, whichinclude: Coronary heart disease, cardiomyopathy, cardiovascular disease,ischaemic heart disease, heart failure, hypertensive heart disease,inflammatory heart disease, valvular heart disease. Also, othercategories of heart diseases are possible to identify and monitor usingthe EMG for biometric assessment. Also, it is believed that thecategories of diseases, as well as the diseases within categories ofvarious organs (not just heart and heart related) and biological systemsare potentially identifiable using the present disclosure.

In addition to medical diagnosis, the use of EMG for biometricassessment can be used to detect or analyze additional data related toan individual. This can be used in any convenient manner as desired bythe subject, and can be used as a tool to analyze the person'sbiological or neurological state.

Basal ganglia dysfunction is believed to play a part in thepathophysiology of obsessive-compulsive disorder (OCD). The use of acomputer aided technique for the analysis of hand movements allows thedetection of subtle motor performance abnormalities, and can be appliedin the study of patients with OCD and healthy controls. In one study, adigitizing graphic tablet was used to study hand motor performance inunmedicated patients with OCD and compared with healthy controls. Allsubjects drew superimposed concentric circles with both the right andthe left hand, in addition to writing a given sentence, their personalsignature, and letter sequences in four different sizes. Kinematicparameters were calculated to quantify hand motion. Subjects with OCDhad significant impairments of handwriting performance, reflected bylower peak velocity and micrographia, as compared with controls andshortened acceleration phases per stroke. By contrast, in repetitivedrawing, subjects with OCD had higher peak velocity than neurotypicalcontrol subjects. There were no significant differences in left andright hand performance between groups. Subjects with early versus lateage of onset differed in handwriting parameters, such as handwritingconsistency. Greater severity of obsessions and compulsions correlatedwith increasingly poor handwriting performance in subjects with OCD.

A subtle motor dysfunction in OCD can be detected with a digitizingtablet. The findings show handwriting impairments in patients with OCD,in line with the assumption that basal ganglia dysfunction is part ofOCD pathophysiology. Repetitive motor pattern performance was notimpaired, but rather tended to be even better in subjects with OCD thanin controls. The findings also support the concept that subjects withOCD with early versus late age of onset differ in pathophysiologicalmechanisms and basal ganglia dysfunction.

Motor disturbances are a relevant aspect of depression. Kinematicalanalysis of movements can be applied to explore which type of motordysfunction is associated with depression.

It is further believed that differences in sex hormones affecthandwriting style. Digit ratio and sex role identity are believed to beinfluenced by prenatal hormone balances and can act as determinants ofthe sex as reflected in handwriting. Accordingly, there is a significantpossibility of biological determinant of the judged gender ofhandwriting. It further found that there is a potential interplaybetween these variables and sex role identification. One biologicalmarker that was identified was 2D:4D digit ratio (of index finger toring finger length), which is negatively associated with prenataltestosterone and positively with prenatal oestrogen. In one example,handwriting samples were found to identify the gender of a statisticallysignificant percentage of test groups. These ratings of handwritinggender correlated significantly with digit ratio. A more conservativeanalysis this time within each sex found that a women's right hand digitratio correlated with relative sexuality of handwriting, but there wasno corresponding relationship for the males. These findings suggest thatprenatal hormonal influences can affect later female handwritingperformance and might even affect developmental inter-hemisphericdifferences, but do not appear to impact on males.

EMG principal component analysis of finger spelling has been used todemonstrate that within a single hand shape transmission, differentmuscles become active at different times and for somewhat differentdurations. Thus, muscle activation waveforms are asynchronous and cannot be adequately described in terms of a single command wave formacting as a common drive to groups of agonists and antagonists. On theother hand, the average EMG levels revealed instances of co-activationand reciprocal activation of muscle pairs. This can suggest that handmovements may be generated by activity waves unfolding in time.

For pattern identification it is important to analyze the EMG activityfrom various muscles. This is because the EMG recordings taken duringmovements of different fingers indicate that a given muscle could act asan agonist, antagonist, or stabilizer of the digits it serves.Furthermore, during a given finger movement, several different musclestypically were active. Therefore, their results indicated thatindividuated finger movements were produced not by independent sets ofmuscles acting on each digit, but by the activity of several muscles,many of which act on more than one digit, combined such that the neteffect was movement of one digit more than others.

Patient Monitoring

EMG has the advantage that it makes it possible to measure changes inthe individual person. In addition, it is possible for an individualwith a medical condition to self-monitor either the condition or theeffects of treatments. By way of example, the individual can use EMGbiometric assessment to monitor the effects of a drug used by thatindividual.

In particular, certain drugs have effects on a patient's physicalactivity, some of which are very visible. For example, it is possible toidentify caffeine withdrawal by a slight shaking, for example whilelifting a lightweight object. A person using some benzodiazapine drugswould have a particular type of jerky hand movement.

It is often difficult for a physician to objectively evaluate apatient's pain or other internal reactions because in many cases, thephysician must rely on subjective data either from the patient or fromdirect observation. EMG could be used to evaluate the reaction, becausethe effect of a particular condition on hand movement as detected by EMGis substantially less subjective. In this manner, the physician wouldhave the ability to use a standardized test, measured by EMG, with theresults being analyzed by the EMG.

Another issue would be that if it is possible to measure an effect of acondition, it is also possible to very quickly analyze a treatment orsupposed “cure”. This becomes particularly true in cases where thecondition is identified by a marker and the treatment has an effect thatextends beyond the effects of that treatment that can be expected absentthe condition being treated. Thus, EMG recorded during targetstandardized repeatable movements can be used in a clinical setting as ameasurement parameter to determine a disease and its severity that maybe done by applying various algorithms. The methods of statisticalanalysis, neural networks, and other pattern recognition techniques maybe useful in medical diagnosis through EMG.

Accordingly, EMG signals can be examined for patterns which arecharacteristic of particular illnesses and conditions. In this sense,the disease or condition will reveal itself on EMG signals that arerecorded during standard handwriting, controlled hand movement oranother target movement, regardless of whether the indications are anapparent part of the subject's “handwriting” or handwriting as reflectedby EMG patterns. In this sense, the patterns are different from patternsused for detection of handwriting patterns per se. Therefore, thehandwriting or other controlled hand movement as reflected by EMGfunctions as a “carrier” for signals used for diagnosis.

The same device can supply the data over time to a larger knowledge basecomputerized system containing all medical and biological informationabout a human body, its organs, and systems that was received from theanalysis of EMG during controlled hand movement activity. A database ofa single person can accept the data from other databases, including ofone's parents for various searches, including identifying “controlledhand movement EMG” patterns of genetic diseases. Databases of variousorgans and systems and larger knowledge bases can be scanned from timeto time and appropriate warnings can be given to a person. Thishistorical data about the body activity and bio status can be locatedlocally or remotely. The output from this medical device can go to otheranalytical databases.

This disclosure relates to the diagnostics of various malfunctions ordiseases of all body organs and interconnecting biological systems in abody. Various examples of organs, injuries, and diseases are providedjust to illustrate the wide range of applications that this disclosurecan be used for. The techniques can be also applied to characterizehealthy people.

Observing Learned Behavior

The subject can be observed during learning in order to measure thesubject's response during the learning process. This provides a means toassess the learning process in the subject, based on quantitativemeasurements obtained by EMG. These measurements can then be used todetermine the learned response. The learned response can be directlytransferred to controlled hand movement, as in learning an alphabet orpattern such as characters in the PalmPilot™ Grafiti™ alphabet, learningto draw or replicate a pattern, or as a measured secondary responsesensed through controlled hand movements during a diverse learningprocess. The EMG is measured or recorded during standardized learningprocedures to provide a quantifiable assessment of the subject'slearning process.

Nerve Sensing

All organs and biological systems are regulated through the nervoussystem. The brain is the center of the nervous system. The human braincontains roughly 100 billion neurons, each linked with up to 10,000synaptic connections. These neurons communicate with one another bymeans of long protoplasmic fibers called axons, which carry trains ofsignal pulses called action potentials to distant parts of the brain orbody and target them to specific recipient cells. In one sense, it mightbe said that the most important function of the brain is to serve as thephysical structure underlying the mind. From a biological point of view,though, the most important function is to generate behaviors thatpromote the welfare of an animal. The brain controls behavior either byactivating muscles, or by causing secretion of chemicals such ashormones. In case of malfunction of a biological system, or internalorgans, the nervous system is going to change its behavior.

Controlled hand movement is controlled by the motor system. Motorsystems are areas of the brain that are more or less directly involvedin producing body movements, that is, in activating muscles. With theexception of the muscles that control the eye, all of the voluntarymuscles in the body are directly innervated by motor neurons in thespinal cord, which therefore is the final common path for themovement-generating system. Spinal motor neurons are controlled both byneural circuits intrinsic to the spinal cord, and by inputs that descendfrom the brain. The intrinsic spinal circuits implement many reflexresponses, and also contain pattern generators for rhythmic movementssuch as walking or swimming. The descending connections from the brainallow for more sophisticated control.

The brain contains a number of areas that project directly to the spinalcord. At the lowest level are motor areas in the medulla and pons. At ahigher level are areas in the midbrain, such as the red nucleus, whichis responsible for coordinating movements of the arms and legs. At ahigher level yet is the primary motor cortex, a strip of tissue locatedat the posterior edge of the frontal lobe. The primary motor cortexsends projections to the subcortical motor areas, but also sends amassive projection directly to the spinal cord, via the so-calledpyramidal tract. This direct corticospinal projection allows for precisevoluntary control of the fine details of movements.

Other “secondary” motor-related brain areas do not project directly tothe spinal cord, but instead act on the cortical or subcortical primarymotor areas. Among the most important secondary areas are the premotorcortex, basal ganglia, and cerebellum.

The premotor cortex (which is actually a large complex of areas) adjoinsthe primary motor cortex, and projects to it. Whereas elements of theprimary motor cortex map to specific body areas, elements of thepremotor cortex are often involved in coordinated movements of multiplebody parts.

The basal ganglia are a set of structures in the base of the forebrainthat project to many other motor-related areas. Their function has beendifficult to understand, but one of the most popular theories currentlyis that they play a key role in action selection. Most of the time theyrestrain actions by sending constant inhibitory signals toaction-generating systems, but in the right circumstances, they releasethis inhibition and therefore allow their targets to take control ofbehavior.

The cerebellum is a very distinctive structure attached to the back ofthe brain. It does not control or originate behaviors, but insteadgenerates corrective signals to make movements more precise. People withcerebellar damage are not paralyzed in any way, but their body movementsbecome erratic and uncoordinated.

In addition to all of the above, the brain and spinal cord containextensive circuitry to control the autonomic nervous system, which worksby secreting hormones and by modulating the “smooth” muscles of the gut.The autonomic nervous system affects heart rate, digestion, respirationrate, salivation, perspiration, urination, and sexual arousal—but mostof its functions are not under direct voluntary control.

One example of a biological system associated with the nervous systemwould be the lungs and central nervous system (CNS), because CO2 levelsin the blood change blood PH, which is sensed as being out of breath.Further breathing itself is controlled by the CNS. This will affect theEMG signals during controlled hand movement activity. Similarly, manydigestive functions interact with the CNS. As a practical matter, thisrelates to the entire body, since general health and things such asexhaustion are known to affect hand movement.

Due to the clinical nature of EMG, the same signals obtained duringcontrolled hand movement may provide a quantifiable way of clinicaldiagnostics. For the most accurate interpretation, EMG signals have togo through the processes of noise reduction, filtration, rectification,integration, amplification, differentiation, and/or other necessarymodifications. All these processes can be described as algorithms. Inaddition, other processing algorithms should be employed in order toidentify repetitive activities, such as patterns of EMG digitalrepresentations. Thus, one would employ preparation, recognition, andclassification, and/or other algorithms to identify patterns derivedfrom the electrical signals, obtained from muscles of hands and/orforearms, etc., via surface sensing electrodes and during the controlledhand movement activity of an individual. These patterns of signalsshould be associated with a particular disease/malfunction, or pluralityof diseases, or malfunctions, when a particular organ or the pluralityof organs, do not function in the normal way for an individual withparticular age and internal, or external conditions.

One of the useful endeavors of controlled hand movement that people weretrying to explore over a long period of time is to understand theindividual characteristics of a person, including their clinicalconditions. Almost every book written on handwriting analysisincorporates at least a paragraph, and often chapters, on clinicalgraphology; however, very few scientific papers seem to have beenpublished by the authors to validate the claims. Alternatively, thisdisclosure is based on the fact that the nature of EMG duringhandwriting or other controlled hand movement is fundamentally differentfrom the nature of mechanical recording of handwriting or similarrepeatable movement. EMG is directly related to the activity of theneurons that are located in a spinal cord. The handwriting processrepresents a dynamic combination of conscious and subconsciousactivities. Therefore, in this disclosure EMG is not merely a media thatcan be used to calculate mechanical movements, but rather it is ameasurable parameter representing a combined state of a clinicalcondition. In this context, EMG that is recorded during handwriting,controlled hand movement or another target movement can be used as anaccurate indicator of an appearance, or a change in a disease, as wellas a predisposition to a disease. Finally, since the behaviors of peoplein some respect is determined by the conditions and developments oftheir organs and biological systems supporting those organs, thisdisclosure can be used for understanding and evaluating the behaviorsand character traits of healthy subjects and finding theirpredispositions, and compatibilities. The examples include, but notlimited to the information about personality, pregnancy, sex of thebaby, etc.

In a broad conceptually, the EMB biometric assessment apparatuscomprises two components; a biosensor and a pattern recognition module.The biosensor includes physical sensors on a garment, such as a glove ora glove with a forearm extension. The sensors are able to read thebiometric signals from the body, using electromyography (EMG). In thecase of a glove, the signals are read from nerve connections to themuscles of a hand, and/or a forearm, using EMG.

The EMG signals are provided to the pattern recognition modules. Thepattern recognition module includes pattern recognition and extractionsoftware. This software will process and interpret the biometric signalsfor the purposes of identifying of a malfunction or a disease of organ,or the plurality of organs in a human body. The same device can beextended to process and interpret additional data related to anindividual at the time when controlled hand movement takes place.Biometric assessment therefore is implemented with a device, a method,and a system for digital recordation, storage, displaying, andcommunication system that may be extended to processing of additionalrelated information, such as the medical condition of the human body.This instantaneous controlled hand movement EMG recording system may becombined with other forms of communication and data processing, andcontrol such as phones (e.g. SMS/MMS messages), medical devices,translators, telerobotics, games, sports, etc.

Sensing of EMG

This process incorporates a method, device and system for reading,interpreting, and processing the EMG related data, obtained duringhandwriting activity, drawing and/or other controlled hand movement.This technique is based on biometric signals. While handwriting analysisis discussed, in cases in which EMG is used for biometric assessment,the actual analysis of the written material or the handwriting forpurposes other than for biometric assessment may be superfluous becausein the case of biometric assessment, the handwriting is a tool.Nevertheless, there may be cases in which the information obtained frombiometric assessment may be used to augment the analysis of handwriting,for example as an assistive tool for a person with ataxia or forfacilitated communication.

It is believed that biometric signals from muscles and other neuralcommunications contain the most complete data pertaining to thecontrolled hand movement activity. This data is used for interpretationwith programming algorithm. The purpose of this controlled hand movementrecordation device is to accurately interpret the medical conditions ofvarious organs in a human body. This recorded data may also be processedto translate the information into human readable format in the samedevice or elsewhere.

The system also includes the pattern recognition algorithm that willinterpret the biometric signals. Another programming algorithm willprocess and translate the information. The program will be capable ofdisplaying a message for a patient and/or a medical professional in areadable format for itself or passing it to a viewer or word processor.The program can also process the received information in any convenientmanner. The user should be able to input some additional information fora device to take into account and use in conjunction with the dataobtained from EMG.

Configuration

In one general aspect, the disclosure incorporates an approach to recordcontrolled hand movement activity, based on a biosensor that is readingthe biometric signals from the muscles and/or neural communications.These biosensors may be located on a surface of a hand including fingersand/or a forearm. The biosensor based device is independent of an actualwriting instrument, and the user can use any writing instrument or afinger to write on any paper or just on a plain surface. The dataprocessing unit and a memory chip for a biosensor maybe also located inan abbreviated glove, a watch, wristband, a sleeve, or some otherarticle of apparel. The biosensor may also collect other biometricsignals, voice, or textual data to be interpreted and combined with acell phone, and/or other devices. The device may be independent fromanother electronic device that will be used to display theinterpretation of biometric signals, in which case, the data can betransmitted to another device at the same time, or at a later time.Prior to transmission, the data may be stored and/or immediatelytransmitted to another electronic device, if needed. In addition, it ispossible to incorporate some basic and/or more sophisticated displayingcapabilities on a recording or storage device used for temporary storageof the biometric data.

This described technique uses the fact that EMG signals can be used todigitize controlled hand movement and effectively creating the device totransfer the controlled hand movement activity into a computer device.This technology allows for a mini glove to record EMG while the userswrite or draw, using fingers, any writing instrument. This recordedinformation may be stored locally in a memory chip and then transferredto a computer, or a cell phone.

The technique is implemented with pattern recognition algorithms thatprovide the correlation between the medical status of human organism andEMG signals. In addition, it takes advantage of dry electrodes appliedto skin locations through a mini glove. This disclosure will be used inconsumer electronics, education, medicine, telerobotics, sports, andmany other fields, since EMG can be interpreted in various domains, liketime, frequency, and dimensions.

The data apparel for instantaneous EMG recording during controlled handmovement uses three design principles:

Dry EMG electrodes

EMG data acquisition system

Pattern recognition algorithms

While dry EMG electrodes provide advantages in terms of convenience ofuse, it is also possible to use electrodes requiring gel connections orother types of wet electrodes.

These design principles can be used in a biomedical application, whereEMG is a measurable parameter of a particular biomedical condition, suchas heart failure. In that case, EMG electrodes can be applied by aprofessional and may or may not include a glove; however, a glove,special sleeve, or similar article of apparel still can be also used forpreliminary tests.

Controlled hand movement activity that is represented by EMG signals iscaptured in a digital format by applying computer algorithms toprocessed (filtered, pretreated and digitized) electromyography (EMG)signals. Pattern classification and recognition algorithms are used inconjunction with pattern recognition techniques. The recording is donethrough a data processing apparatus and routine, handwriting, controlledhand movement or another target movement interpretation routine, and aroutine that generates an output.

Applying computer algorithms to EMG signals that are recorded duringhandwriting, controlled hand movement or another target movement mayhelp in diagnosis of various diseases, and monitoring a rehabilitationor recovery process. Finally, since the behaviors of people in somerespect is determined by the conditions and developments of their organsand biological systems supporting those organs, this disclosure can beused for understanding and evaluating the behaviors and character traitsof healthy subjects and finding their characteristics, predispositions,and compatibilities.

Mathematical Filtering Techniques

EMG and hand gesture recognition can be performed by collecting surfaceEMG signals using dry electrodes. In theory, muscle activity originatingfrom different muscles can be considered independent and this gives anargument to the use of Independent Component

Analysis (ICA) for separation of muscle activity originating from thedifferent muscles. The outcome of ICA is that the signals are separatedwithout there being any information of the order of the sources. Inaddition to ICA the activities of different muscles are classified usingback-propagation neural networks. Dry electrodes that are used to sensethe EMG data from the surface of the body have to be very sensitive, lowmaintenance, and should not require any skin preparation.

The data acquisition system has to be computation and energy efficientwith a very small footprint. In a normal use it is enough to havesurface electrodes inside of the appropriate apparel. Sometimes,internal electrodes will be required.

In the past, when EMG was used to identify movement, the systems werelimited as suitable for only gross actions, and when there was oneprime-mover muscle involved. The present technique uses signalprocessing and mathematical models and thereby makes it practical toimplement advanced EMG detection and analysis techniques. Variousmathematical techniques and Artificial Intelligence (AI) have receivedextensive attraction. Mathematical models include wavelet transform,time-frequency approaches, Fourier transform, Wigner-Ville Distribution(WVD), statistical measures, and higher-order statistics. AI approachestowards signal recognition include Artificial Neural Networks (ANN),Dynamic Recurrent Neural Networks (DRNN), and fuzzy logic systems.

A variety of mathematical filtering techniques can be used to enhanceEMG analysis. The accuracy of hand gesture recognition using surface EMGincludes statistical analysis and neural networks for applications inprosthetic devices, to provide an intelligent and simple system torecognize fairly complex hand movements and provide a user assessmentroutine to evaluate the correctness of executed movements. This can beimplemented using an Adaptive Neuron-Fuzzy Inference System (ANFIS)integrated with a real time learning scheme to identify hand motioncommands. The ANFIS method can be utilized as part of a hybrid methodfor training the fuzzy system, comprising Back Propagation (BP) andLeast Mean Square (LMS). A subtractive clustering algorithm can then beused in order to optimize the number of fuzzy rules. In order to designa pattern recognition system Time Domain (TD) and time FrequencyRepresentation (TFR) may be used, and in order to decrease theundesirable effects of the dimension of these feature sets, PrincipleComponent Analysis (PCA) may be utilized.

Monte-Carlo tree search can be used in combination with EMG data fordiagnostics of various diseases and organs malfunctions. Also, these arejust the examples of the algorithms that can be used to identify medicalpatterns. Variations and combinations of these and additional algorithmscan be used as well. Linear Discriminant Analysis, Bayesian methods,hidden Markov chains, Fourier analysis, Fast Fourier Transform (FFT),adaptive neuro-fuzzy inference system, adaptive signal processing, backpropagation, least mean square, Artificial Neural Network (ANN),autoregressive, mean absolute value, slope sign changes, zero crossing,principal component analysis, common mode rejection ratio, membershipfunction, time domain (features), time frequency representations, fuzzyinterference system, discrete wavelet transform, root mean square.

Pattern Recognition Algorithms

A raw sensed output of EMG signals does not make any sense asinterpretations of hand movement. It is only when one applies EMGinterpretation algorithms to the raw sensed output that one is able tointerpret the signals as a meaningful activity and find thecorrespondence with the fine grained hand movements. This is becausethere is no direct correspondence between sensed EMG of muscles and finegrained hand movements associated with the sensed EMG. Additionally, allmuscles generate EMG signals, irrespective of whether the fingers aremoving or not.

Pattern recognition aims to classify data (patterns) based on either ana priori knowledge or on statistical information extracted from thepatterns. The patterns to be classified are usually groups ofmeasurements or observations represented as data points in anappropriate multidimensional space.

A complete pattern recognition system includes a sensor that gathers theobservations to be classified or described; a feature extractionalgorithm that computes numeric or symbolic information from theobservations; and a classification or description scheme that does theactual job of classifying or describing observations, relying on theextracted features.

The classification or description scheme is usually based on theavailability of a set of patterns that have already been classified ordescribed. This set of patterns is termed the training set and theresulting learning strategy is characterized as supervised learning.Learning can also be unsupervised, in the sense that the system is notgiven an a priori labeling of patterns, instead it establishes theclasses itself based on the statistical regularities of the patterns.

The classification or description scheme usually uses one of thefollowing approaches: statistical (or decision theoretic), or syntactic(or structural). Statistical pattern recognition is based on statisticalcharacterizations of patterns, assuming that the patterns are generatedby a probabilistic system. Structural pattern recognition is based onthe structural interrelationships of features. A wide range ofalgorithms can be applied for pattern recognition, from very simpleBayesian classifiers to much more powerful neural networks. Furtherinformation is found in, Sergios Theodoridis, Konstantinos Koutroumbas,Pattern Recognition (3rd edition, 2006)

Multiple Linear Regression

In statistics, multiple linear regression is a regression method ofmodeling and predicting the conditional expected value alone variable ygiven the values of some other variable or variables x. Linearregression is called “linear” because the relation of the response tothe explanatory variables is assumed to be a linear function of someparameters. It is often erroneously thought that the reason thetechnique is called “linear regression” is that the graph of y=α+βx is aline. In contrast, if the model is, by way of example:y _(i) =α+βx _(i) +γx _(i) ²+ε_(i)(in which case the vector (x_(i), x_(i) ²) is placed the role formerlyplayed by x_(i) and the vector (β,γ) is placed in the role formerlyplayed by β), then the problem is still one of linear regression, eventhough the graph is not a straight line.

Regression models which are not a linear function of the parameters arecalled nonlinear regression models (for example, a multi-layerartificial neural network).

More generally, regression may be viewed as a special case of densityestimation. The joint distribution of the response and explanatoryvariables can be constructed from the conditional distribution of theresponse variable and the marginal distribution of the explanatoryvariables. In some problems, it is convenient to work in the otherdirection: from the joint distribution, the conditional distribution ofthe response variable can be derived. Regression lines can beextrapolated, where the line is extended to fit the model for values ofthe explanatory variables outside their original range.

Bayesian Classifier

Another pattern recognition model is called Bayesian classifier. This isa probabilistic classifier based on applying Bayes' theorem with strongindependence assumptions. In spite of their naive design and apparentlyover-simplified assumptions, naive Bayesian classifiers often work muchbetter in many complex real-world situations than might be expected.Recently, careful analysis of the Bayesian classification problem hasshown that there are sound theoretical reasons for the seeminglyunreasonable efficacy of naive Bayesian classifiers, Abstractly, theprobability model for a classifier is a conditional model:p(C|F ₁ , . . . ,F _(n))over a dependent class variable C with a small number of outcomes orclasses, conditional on several feature variables F₁ through F_(n). Theproblem is that if the number of features, n, is large or when a featurecan take on a large number of values, then basing such a model onprobability tables is infeasible and computationally demanding. Themodel is therefore reformulated to make it more tractable.

Using Bayes' theorem, we write

${p\left( {{C❘F_{1}},\ldots\mspace{14mu},F_{n}} \right)} = {\frac{{p(C)}{p\left( {F_{1},\ldots\mspace{14mu},{F_{n}❘C}} \right)}}{p\left( {F_{1},\ldots\mspace{14mu},F_{n}} \right)}.}$

In practice only the numerator of that fraction is of interest, sincethe denominator does not depend on C and the values of the features F₁are given, so that the denominator is effectively constant. Thenumerator is equivalent to the joint probability modelp(C,F ₁ , . . . ,F _(n))which can be rewritten as follows, using repeated applications of thedefinition of conditional probability:

$\begin{matrix}{{p\left( {C,F_{1},\ldots\mspace{14mu},F_{n}} \right)} = {{p(C)}{p\left( {F_{1},\ldots\mspace{14mu},{F_{n}❘C}} \right)}}} \\{= {{p(C)}{p\left( {F_{1}❘C} \right)}{p\left( {F_{2},\ldots\mspace{14mu},{F_{n}❘C},F_{1}} \right)}}} \\{= {{p(C)}{p\left( {F_{1}C} \right)}{p\left( {{F_{2}❘C},F_{1}} \right)}}} \\{p\left( {F_{3},\ldots\mspace{14mu},{F_{n}❘C},F_{1},F_{2}} \right)} \\{= {{p(C)}{p\left( {F_{1}❘C} \right)}{p\left( {{F_{2}❘C},F_{1}} \right)}{p\left( {{F_{3}❘C},F_{1},F_{2}} \right)}}} \\{p\left( {F_{\downarrow},\ldots\mspace{14mu},{F_{n}❘C},F_{1},F_{2},F_{3}} \right)}\end{matrix}$and so forth. Now the “naive” conditional independence assumptions comeinto play: assume that each feature F_(i) is conditionally independentof every other feature F_(j) for j≠i. Therefore:p(F _(i) |C,F _(j))=p(F _(i) |C)and so the joint model can be expressed as:

$\begin{matrix}{{p\left( {C,F_{1},\ldots\mspace{14mu},F_{n}} \right)} = {{p(C)}{p\left( {F_{1}❘C} \right)}{p\left( {F_{2}❘C} \right)}{p\left( {F_{3}❘C} \right)}\mspace{14mu}\ldots}} \\{= {{p(C)}{\prod\limits_{i = 1}^{n}{{p\left( {F_{i}❘C} \right)}.}}}}\end{matrix}$

Under the above independence assumptions, the conditional distributionover the class variable C can be expressed according to:

${p\left( {{C❘F_{1}},\ldots\mspace{14mu},F_{n}} \right)} = {\frac{1}{Z}{p(C)}{\prod\limits_{i = 1}^{n}{p\left( {F_{i}❘C} \right)}}}$where Z is a scaling factor dependent only on F₁, . . . , F_(n), i.e., aconstant if the values of the feature variables are known.

Models of this form are much more manageable, since they factor into aso-called class prior p(C) and independent probability distributionsp(F_(i)|C). If there are k classes and if a model for p(F_(i)) can beexpressed in terms of r parameters, then the corresponding naive Bayesmodel has (k−1)+n r k parameters. In practice, often k=2 (binaryclassification) and r=1 (Bernoulli variables as features) are common,and so the total number of parameters of the naive Bayes model is 2n+1,where n is the number of binary features used for prediction. Furtherinformation is found in, Domingos, Pedro & Michael Pazzani, “On theoptimality of the simple Bayesian classifier under zero-one loss”.Machine Learning, 29:103-137, (1997).

Despite the fact that the far-reaching independence assumptions areoften inaccurate, the naive Bayesian classifier has several propertiesthat make it very useful in practical applications. In particular, thedecoupling of the class conditional feature distributions means thateach distribution can be independently estimated as a one dimensionaldistribution. This in turn helps to alleviate problems stemming from thecurse of dimensionality, such as the data sets that scale exponentiallywith the number of features. Like all probabilistic classifiers underthe MAP decision rule, it arrives at the correct classification as longas the correct class is more probable than any other class; hence classprobabilities do not have to be estimated very well. Thus, the overallclassifier is robust enough to cope with the deficiencies in itsunderlying naive probability model. Further information is found in,Hand, D J, & Yu, K. “Idiot's Bayes—not so stupid after all?”International Statistical Review, Vol 69 part 3, (2001), pages 385-399.

Artificial Neural Network

An artificial neural network (ANN), such as Time Lagged RecurrentNetwork (TLRN) or commonly just neural network (NN) is an interconnectedgroup of artificial neurons that uses a mathematical model orcomputational model for information processing based on a connectionistapproach to computation. In most cases an ANN is an adaptive system thatchanges its structure based on external or internal information thatflows through the network. (The term “neural network” can also meanbiological-type systems.)

In more practical terms neural networks are non-linear statistical datamodeling tools. They can be used to model complex relationships betweeninputs and outputs or to find patterns in data.

There is considerable overlap between the fields of neural networks andstatistics. Statistics is concerned with data analysis. In neuralnetwork terminology, statistical inference means learning to generalizefrom noisy data. Some neural networks are not concerned with dataanalysis (e.g., those intended to model biological systems) andtherefore have little to do with statistics. Some neural networks do notlearn (e.g., Hopfield nets) and therefore have little to do withstatistics. Some neural networks can learn successfully only fromnoise-free data (e.g., ART or the perception rule) and therefore wouldnot be considered statistical methods. Most neural networks that canlearn to generalize effectively from noisy data have at least somesimilarity in technique with statistical methods.

Hardware Implementation

FIG. 1 is a diagram illustrating exemplary circuit 100 used to senseelectromyographic (EMG) data and provide an output based on the senseddata. The circuit 100 detects biometric signals obtained from sensingstimulation of muscles. It can be seen that these signals are distinctand not chaotic. There is a correlation between the muscle activitiesand movements of fingers, hand, and arm via programming algorithms,e.g., pattern recognition. The special pattern recognition algorithmsare required, because the correlation is not direct. The controlled handmovement recordation device contains the biosensor that may be in a formof MEMS system as it should be very small. A biosensor also can be anelectrode for recording Electromyography (EMG) signals. This sensor mayhave an amplifier to increase the signal to noise ratio. The device willhave a way to store the data after it is read and/or to transmit thedata to where it will be processed for interpretation and/or displayed.Intermediate systems may also be included for other purposes.

Depicted in FIG. 1 is a plurality of sensor circuits 111, 112, 113, 114,a multiplexer 121, an analog-to-digital converter 125, and a processingunit 127. Also depicted is a wireless communication transceiver 141which can communicate with an external computer 147. FIG. 2 is a diagramshowing details of one of the sensor circuits 111 depicted in FIG. 1.The sensor unit includes a sensor electrode 211, an amplifier 213 and anoutput filter 215.

FIG. 3 is a diagram showing an example of a user interface garment 301.The garment can be in the form of a lightweight glove or “mini glove”,such as the “fingerless glove” depicted. The glove has attached theretoa set of sensing electrodes 321, 322, 323, 324, 325, 326 and acorresponding set of electrical connections 331. The electricalconnections are connected to a processing module 341, which includes thecircuitry depicted in FIG. 1.

It is understood that the specific structure of the glove is not part ofthe disclosure. It is also possible to provide any convenient form ofattachment for the electrodes, not limited to a glove-like structure. Itis further possible to mount one or more of the sensing electrodes321-326 to parts of the body separate from the hand (e.g., on the arm),and it is possible to mount one or more of the sensing electrodes321-326 separate from the glove. The use of five electrodes is presentedas an example, for clarity of the drawings and does not represent theexpectations of actual construction.

It is anticipated that the actual locations of the electrodes willdiffer from that shown in the diagram, as the locations are selected foroptimal sensing. Examples of locations which may be selected includearea between the thumb and first finger on the palm side, on the back ofthe hand between the thumb and first finger, on the back of the hand, onthe bony part in alignment with the fourth finger, and on the wrist onfront and back sides of the hand these locations are given by way ofexample, as the actual locations are in accordance with optimal EMSsensing.

The components of the sensors can be combined as a chipset whichincludes one or more monolithic integrated circuit chips. Referring toFIG. 1, such a chipset can include one or more of the multiplexer 121,analog-to-digital converter 125, processing unit 127 and wirelesscommunication transceiver 141. The chipset can further include one ormore components of the sensors 111-114, such as amplifier 213 and outputfilter 215. Similarly, the components of the sensors 111-114 such assensor electrode 211, amplifier 213 and output filter 215 can beintegrated. It is also possible to integrate components associated withthe external computer 147 into the chipset. FIG. 2 is a diagram showingdetails of one of the sensor circuits 111 depicted in FIG. 1. The sensorunit includes a sensor electrode 211, an amplifier 213 and an outputfilter 215.

Dry EMG electrodes

Electrodes for measuring biopotentials are extensively used in modernclinical and biomedical applications. These applications includenumerous physiological tests including electrocardiography (ECG),electroencephalography (EEG), electrical impedance tomography (EIT),electromyography (EMG) and electro-oculography (EOG). The electrodes forthese types of physiological tests function as a transducer bytransforming the electrical potentials or biopotentials within the bodyinto an electric voltage that can be measured by conventionalmeasurement and recording devices. One form of dry electrode is apolysiloxane electrode, described by Klaus-Peter Hoffmann, and RomanRuff, Flexible dry surface-electrodes for ECG long-term monitoring,Proceedings of the 29th Annual International Conference of the IEEE EMBSCite Internationale, Lyon, France Aug. 23-26, 2007, IEEE Document No.1-4244-0788-5/07. The described material is based on a medicallyapproved polysiloxane framework (Pt catalyzed) loaded with conductivenano-particles to realize the electronconductive component. To improvethe electrode-to-skin impedance a general-purpose electrolyte part wasadded to provide the ion-conductivity.

In general, most commercial physiological electrodes for theseapplications today are placed on the surface of the skin. Because ofsuch use, it is important to understand the anatomy of the skin toaddress the problems encountered with these electrodes. The skin is alayered structure, which includes the epidermis and the dermis. Thedermis contains the vascular and nervous components. Further it is thepart of the skin where pain has its origins. The epidermis is the mostimportant layer in the electrode/skin interface. The epidermis comprisesa number of layers. These layers include: [0114] a) Stratum basale orstratum germinativum, which contains living basal cells, that grow anddivide, eventually migrating into the other layers of the epidermis;[0115] b) Stratum spinosum, which contains living cells that havemigrated from the stratum basale. The early stages of desmosomes can befound in this layer; [0116] c) Stratum granulosum, which contains cellswith many desmosomal connections, forms a waterproof barrier thatprevents fluid loss from the body; [0117] d) Stratum lucidum, which is atransition layer between the stratum granulosum and the stratum corneum.It is thickest in high friction areas such as the palms and the soles ofthe feet; and [0118] e) Stratum corneum, which is the outer layer,contains dry, dead cells, flattened to form a relatively continuous thinouter membrane of skin. The deeper cells of this layer still retain thedesmosomal connections, but as they are pushed toward the surface bynewly formed cells in the underlying layers, the junctions graduallybreak and the cells are lost.

The stratum corneum is the primary source of high electrical impedance.This is because dead tissue has different electrical characteristicsfrom live tissue, and has much higher electrical impedance. Thus, thislayer dramatically influences the biopotential measurements. The stratumcorneum is estimated to be approximately one tenth the thickness of theepidermis except for the palms of the hand and the foot where this layeris much thicker. The stratum corneum, further, is very thin and uniformin most regions of the body surface ranging from 13 to 15.mu.m with amaximum of about 20.mu.m. If the high impedance results from the stratumcorneum can be reduced, a more stable electrode will result. Thereforewith existing physiological electrodes, the skin is prepared prior toapplication when lower impedance is required.

Use of Alternative Electrodes Possible

The most common electrode preparation methods that cope with the highimpedance effects of the stratum corneum are: 1) shaving the hair fromthe skin; and either 2a) abrading the stratum corneum or 2b) using anelectrolytic gel. The use of an electrolytic gel or fluid is oftenreferred to as “wet” electrodes. Hair is shaved from the skin to improvethe contact between the electrodes and the skin surface. The goal of theabrasion of the stratum corneum is to reduce the thickness of (orremove) the stratum corneum (and therefore its electrically insulatingcharacteristics). Drawbacks of abrading the skin are that the abradedarea regenerates dead cells fairly quickly (resulting in a limited timeperiod for using the electrode), and if the abrasion is too deep theperson can experience pain. Additionally, electrolytic gels or fluidsmay be applied to abraded surface to enhance the contact. Alternatively,electrolytic gels or fluids can be applied to the surface of the skindirectly. The electrolytic gel having a high concentration of conductiveions diffuses into the stratum corneum and improves its conductivity.Drawbacks observed with the use of electrolytic gels or fluids involvethe change of conductivity with time as the gels dry, discomfort (anitching sensation) at the patients skin as a result of the gels drying,and the possibility of a rash due to an allergic reaction to theelectrolytic gels.

In addition to the inconvenience of “wet” electrodes, “wet” electrodeshave other disadvantages. These include the need for skin preparationand stabilization of the electrode with respect to the skin surface.This is because movement of the electrode on the surface of the skincauses the thickness of the electrolytic layer (formed by theelectrolytic gels or fluids) to change resulting in false variation inthe measured biopotential. Some electrode designs have an adhesivebacking and/or grated surfaces to reduce the movement of the electrodeon the skin surface; however, neither of these features eliminatescompletely the movement of the electrode with respect to the subject'sskin. Another drawback is the length of time required to prepare theskin and apply the electrolytic gels or fluids prior to measurement ofthe biopotentials. Nevertheless, it is possible to use “wet” electrodesfor sensing hand movement.

A less common type of physiological electrode is a non-polarizable “dry”electrode. These ceramic, high sodium ion conducting electrodes do notneed an electrolytic gel before their application. The recordings usingthese physiological electrodes are based on a sodium ion exchangebetween the skin and the electrode. The skin-electrode impedance ofthese type of electrodes are found to decrease as a function ofapplication time. This is a result of perspiration being produced by thebody under the electrode almost immediately after application of theelectrode on the skin. Drawbacks, however, are similar to those of “wet”electrodes.

Another less common type of physiological electrode is an active “dry”electrode with an amplifier. Advances in solid-state electronictechnology have made it possible to record surface biopotentialsutilizing electrodes that can be applied directly to the skin withoutabrading the skin or using an electrolytic gel. These electrodes are notbased on an electrochemical electrode-electrolyte interface. Rather,these electrodes are active and contain a very high impedance-convertingamplifier. Some claim that by incorporating the highimpedance-converting amplifier into the electrode, biopotentials can bedetected with minimal or no distortion. Further information is found in,Babhk Alizadeh-Taheri et al., An Active Microfabricated Scalp ElectrodeArray for EEG Recording Sensors and Actuators, A54, pp. 606-611,Elsevier Science, S. A. (1996); in, Edward D. Flinn, “Ouch-lessInjections”, Popular Science, October 1998, p. 33 United States; in,Patrick Griss et al., “Micromachined Electrodes for BiopotentialMeasurements”, Journal of Microelectromechanical Systems, March 2001,pp. 10-16, vol. 10.

EMG Data Acquisition System

Electromyography (EMG) is an electrophysiological technique forevaluating and recording physiological properties of muscles at rest andwhile contracting. EMG is performed using a device called anelectromyograph, to produce a record called an electromyogram. Anelectromyograph detects the electrical potential generated by musclecels when these cells contract, and also when the cells are at rest. Theelectrical source is the muscle membrane potential, about −70 mV. Due tothe applied method the resulting measured potentials range between lessthan about 50.mu.V to about 20 to about 30 mV. Amplitudes of EMG signalrange between 0 to 10 mV (peak-to-peak), or 0 to 1.5 mV (rms). Thefrequency of the EMG signal is between 0 to 500 Hz. The usable energy ofEMG signal is dominant between 50-150 Hz.

EMG data are routinely acquired in clinical and laboratory settings. Thedetails of these procedures can be found in the works of Carlo Deluca,among others. Further information is found in, De Luca, C. J.Electromyography; and in, Encyclopedia of Medical Devices andInstrumentation, (John G. Webster, Ed.) John Wiley Publisher, 98-109(2006).

The following factors should be considered:

Boost signal to TTL standard level (±5 V.), Noise/Artifact problem,Filter, stability of electrodes attached to skin, proper grounding, DCoffset or bias problem that requires Bias adjustment.

The following are EMG measurement stages:

Hand->Preamplifier->RC Filter->Amplifier with Bias Adjustment->A/DConverter->EMG Capture Program.

Analyses of EMG Modulations and Interactions

Reconstruction of controlled hand movement patterns from the EMGs may beconducted in two steps. During the first step an appropriatemathematical algorithm is trained. This training involves adjusting thealgorithm parameters. This step is often referred to as “fitting”.During the second step, the parameters of the algorithm are fixed, andpredictions are produced from a new segment of EMG records.

Some attempts to reconstruct behavioral variables from muscle activitywere described in, Lebedev M A, Nicolelis M A., “Brain-MachineInterfaces: Past, Present and Future”, Trends Neurosci. 2006 September;29(9):536-46. Epub 2006 Jul. 21 Review. The performance of the linearmodel that predicts the parameters of interest as a weighted linearcombination of input signals:

${V(t)} = {b + {\sum\limits_{\tau = {- m}}^{n}{{w(\tau)}{n\left( {t + \tau} \right)}}} + {ɛ(t)}}$where n(t+τ) is a vector of input signals (EMG), at time t and time-lagτ (negative lags correspond to past events), V is the parameter ofinterest at time t, w(τ) is a vector of weights for each input attime-lag τ, b is the y-intercept, and ε(t) is the residual error. Thisequation is solved using linear least squares regression.

To obtain predictions, EMG signals will be full-wave rectified andband-pass filtered in the range of 0.2-20 Hz, A sample number of timelags with temporary time spacing are used, for example 10 time-lagspreceding the measurement with temporally spaced at 20-100 ms. Thequality of predictions obtained using the EMGs of different muscles willbe evaluated by calculating predictions for individual muscles andseveral muscles in different combinations. 5-10 minutes of data will beused to fit the model and find the weights, w(τ).

The X and Y coordinates of the writing instrument are first predictedand fit to the writing instrument while the subjects make handnotes.Predictions will be calculated for a different 5-10 minute epoch. Thequality of predictions will be evaluated as the eucledian distancebetween the actual and predicted traces of the writing instrument.Further information is found in, Kim H K, Biggs S J, Schloerb D W,Carmena J M, Lebedev M A, Nicolelis M A, Srinivasan M A., “ContinuousShared Control for Stabilizing Reaching and Grasping with Brain-MachineInterfaces”, IEEE Trans Biomed Eng. 2006 June; 53(6):1164-73.

As discussed, other pattern recognition computer algorithms may also beapplied in any combination.

Further Sensed Data

The human hand is a complex system, with the large number of degrees offreedom, somatosensory receptors embedded in its structure, actuatorsand tendons, and a complex hierarchical control. Despite thiscomplexity, the user can carry out the different movements virtuallyeffortlessly (after an appropriate and lengthy training). Scientists andengineers made a lot of effort to replicate a sensory-motor function ofthe human hand, a complex and adaptive system capable of both delicateand precise manipulation and power grasping of heavy objects. Most ofthese efforts were spent in the area of prosthetic devices andrehabilitation techniques. These efforts led to much greaterunderstanding of general EMG applications. Further information is foundin, M. Zecca, S, Micera, M. C. Carrozza, & P. Dario, “Control ofMultifunctional Prosthetic Hands by Processing the ElectromyographicSignal”, Critical Reviews in Biomedical Engineering, 30(4-6):459-485(2002).

Fundamental insights into how arrays of neurons encode motor or sensoryvariables can be gained from computational methods that attempt toreconstruct or predict aspects of animal behavior or sensory stimulifrom the recorded activity of neural populations. The accuracy withwhich a behavior such as the direction of limb movement or the path ofan animal navigating a maze can be reconstructed provides an estimate ofthe amount of behaviorally relevant information represented in thedischarge of the recorded neurons. It should also be possible to invertthis process to predict neural activity from behavior. One applicationof such an approach would be to identify the patterns of neuromuscularactivity across a population of muscles needed to elicit desiredmovements in paralyzed individuals using functional electricalstimulation. Further information is found in, Robert E. Kass, ValerieVentura and Emery N. Brown, “Statistical Issues in the Analysis ofNeuronal Data”, J Neurophysiol 94:8-25, 2005. doi:10.1152/jn.00648.2004;in, R. M. Davies, G. L. Gerstein and S. N. Baker, “Measurement ofTime-Dependent Changes in the Irregularity of Neural Spiking”, JNeurophysiol, Aug. 1, 2006; 96 (2): 906-918; in, Robert E. Kass, Valerie Ventura, and Emery N. Brown, “Statistical Issues in the Analysis ofNeuronal Data”, J Neurophysiol 94: 8-25, 2005;doi:10.1152/jn.00648.2004; and in, Nicolelis M A L, Ghazanfar A A,Stambaugh C R, Oliveira L M O, Laubach M, Chapin J K, Nelson R J, Kaas JH, “Simultaneous Encoding of Tactile Information by Three PrimateCortical Areas”, Nat Neurosci 1:621-630 (1998).

Functional electrical stimulation involves artificial activation ofparalyzed muscles with electrodes and has been used successfully toimprove the ability of quadriplegics to perform activities for dailyliving. The range of motor behaviors that can be generated by functionalelectrical stimulation, however, is limited to a relatively small set ofmovements, such as hand grasp and lateral and palmer pinch. A broaderrange of movements has not been implemented primarily because of thesubstantial challenge associated with identification of the patterns ofmuscle stimulation needed to elicit specified movements. Most limbmovements, even those involving a single digit, require intricatecoordination among multiple muscles that act across several joints. Suchcomplex mechanical systems do not readily lend themselves todeterministic solutions. Although EMG signals recorded from able-bodiedsubjects can be used to identify patterns of muscle activity associatedwith a particular movement, this painstaking method yields controlsignals appropriate only for the motor task from which the EMG signalswere originally recorded. In an attempt to overcome this limitation itis possible to use Bayes' theorem to predict the patterns of musclestimulation needed to produce, in theory, an unlimited set of movementsacross multiple joints. The bidirectionality of Bayes' theoremfacilitated the inverse prediction of neuromuscular activity frombehavior. Further information is found in, Heather M. Seifert and AndrewJ. Fuglevand, “Restoration of Movement Using Functional ElectricalStimulation and Bayes' Theorem”, The Journal of Neuroscience, Nov. 1,2002, 22(21):9465-9474.

Functional Implementation

FIG. 4 is a diagram depicting a functional implementation of a system400 to record a handwriting activity. The system includes means 401 forregistration of electromyography (EMG) signals from one or more musclesat selected locations, which may include the sensors 111-114 (FIG. 1) aswell as the remaining components of the circuit 100 used to sense EMGdata. The means 401 for registration of EMG signals may comprisesensors, electrodes, amplifiers, bandpass filters, multiplexer, analogto digital converter, processing unit, and the means for registration ofEMG signals may provide a routine to improve signal to noise ratio. Alsoincluded are means 403 for reconstructing handwriting to a digitalformat from the EMG signals, depicted as computer 147 (FIG. 1), althoughsome or all of these functions can be provided by processor 127. Thedigital format includes both a visual representation of thereconstructed handwriting and a digitized rendition, such as text. Thesystem 400 includes means 407 for generating a display corresponding tothe digital format, depicted as computer 147 (FIG. 1).

The means 403, 407 for reconstructing and for generating a displaycorresponding to the digital format can generate a display ofmachine-editable text corresponding to the digital format and in thecase of a drawing generate the drawing in an editable format.

Some configurations enhance the interaction of sensed EMG signals withcreation of handwriting images and computer recognition of handwriting.There are various modifications that can be made, including using theEMG signals directly in the recognition of handwriting, “training” and“learning” of a computer application for recognition of handwriting, andthe use of shorthand/or shortcuts for transcription purposes.

FIG. 5 is a diagram depicting a functional implementation of a system500 to perform biometric assessment. The system generally follows thestructure of the system used to perform handwriting analysis (FIG. 4),and may use handwriting as a form of controlled hand movement. Thesystem includes means 501 for registration of electromyography (EMG)signals from one or more muscles at selected locations, which mayinclude the sensors 111-114 (FIG. 1) as well as the remaining componentsof the circuit 100 used to sense EMG data. The means 501 forregistration of EMG signals may comprise sensors, electrodes,amplifiers, bandpass filters, multiplexer, analog to digital converter,processing unit, and the means for registration of EMG signals mayprovide a routine to improve signal to noise ratio. Also included aremeans 503 for providing biometric assessment signals from the EMGsignals, depicted as computer 147 (FIG. 1). The biometric assessmentsignals can then be analyzed, for example using computer 147, and someor all of these functions can be provided by processor 127. The system500 includes means 507 for analyzing the biometric assessment signals,and means 509 for generating a display or output corresponding to thedigital format, depicted as computer 157 (FIG. 1).

The means 503, 507, 509 for performing biometric analysis and forgenerating a display corresponding to the digital format can furtheranalyze the biometric assessment signals to provide data orinterpretation of data, or biometric assessment signals can be readexternally.

The invention includes an approach for synchronizing recordings of EEG,EMG, and handwriting traces using trials in such way that EEG and EMGdata is synchronized according to a press down signal made by a pen orstylus on a tablet that digitizes handwriting traces. A digitizer ofhandwriting traces can be a tablet, or any other system that captureshandwriting traces in a digital format. The inventors discovered thatwith this approach that recordings for control (healthy, non-PD)patients have different correlations characteristics for EMG and EEGsignals compared to PD patients. Control and PD patients were tested bymaking repeated writings of a single character or number, e.g. “3”.Both, EEG and EMG data flows were synchronized with the digitizer ofhandwriting traces. In particular, the inventors also discovered fourbiomarker as follows:

-   1. Control patients had EMG signals with defined short burst of high    intensities. PD patients had EMG signals with evenly distributed    intensities without distinctive short bursts.-   2. Control patients demonstrated a relatively high correlation    between two EMG signals from channels on hand muscles. PD patients    had low correlations for the same channels.-   3. In patients with Parkinson's disease we observed a growth of    correlations in cortex areas activated during handwriting-   4. Control patients had EEG signals with correlations that lasted    for short time intervals. PD patients had EEG signals with    correlations which endured over longer time intervals.

We believe the biomarkers can be used in systems, devices and a methodsfor detecting, analyzing or treating neurological disorders. We havediscovered and claim two types of devices. One device uses off-the-shelfequipment and suitable software for data acquisition and synchronizationof data from multiple channels. Another device used dedicated hardwareand software for data acquisition and synchronization.

Both devices, used either alone or in combination, are beneficial fordiagnosis not only Parkinson's Disease (PD), but also otherneurodegenerative diseases such as Essential Tremor Disorder,Alzheimer's disease, Multiple Sclerosis, Encephalitis, Meningitis,Tropical Spastic Paraparesis, Arachnoid Cysts, Huntington's, Locked-InSyndrome, and Tourette's. In a similar manner, the methods and apparatusdisclosed herein may also be useful to acquire feedback for deep brainstimulation and brain surgeries. Today such procedures are donerelatively blindly by surgeons and may require one or morepost-operative adjustments. However, our methods and apparatus provideobjective measurements that may enable a surgeon, physician, researcheror clinician to gain insight into the efficacy of the deep brainstimulation or surgery.

Laboratory Setup

Handwriting [1] provides an excellent methodological tool forquantitative studies of statistical and correlation properties ofbio-signals. First, it consists of stereotyped hand movements thatinvolve two basic motor components: firmly holding a pen by the fingersand moving the hand and the fingers to produce written text. Second,handwriting is tracked using a laboratory setup with a pen or stylus anda digitizing tablet which allows one to compare handwriting trials,which are well synchronized with respect to each other. Suchcompatibility synchronization of time-dependent data is crucial fortrial-to-trial statistical and correlation analysis.

FIG. 6 shows the block diagram of the laboratory system which reliedupon an off-the-shelf EEG system and a commercial digitizer 310. Itincluded a LogiManage digitizer 601 for digitizing handwriting tracesthat produced X and Y coordinates, as well as “press on paper” signal.EEG signals were recorded on a dedicated computer 650, which wasconnected to an EEG recording system 651 via a printer cable. The datafrom EEG recording system was saved in files on EEG dedicated computer650. The EMG signals were taken from a glove 301 that was equipped withsensors as shown in FIG. 8. The sensor circuits 111 detected andamplified the signals. The sensor circuits were connected by a BNCadapter 130 to an A/D converter 124. The EMG signals were amplified andthen converted into digital signals by the Analog to Digital Converter(ADC) 124 which was connected to an EMG dedicated computer 141 via oneof the computer's USB port. The ADC 124 converts the real world subjectand patient biometric signals into biometric signal data that may beinput to and stored in the memory of a computer. The computer 141 hadsoftware for implementing the steps shown in FIGS. 4 and 5. The subjectswrote the number “3” on the digitizer 601. The start and finish of eachevent was detected by the Pen Down and Pen Up output signals of thedigitizer 601. The EEG files and the EMG files were synchronized withthe Pen Down and Pen Up output signals and their time stamps that weused to determine the trial intervals.

An EEG recording system 651 was used to sense, capture, and record EEGsignals. Such EEG recording systems are commercially available from anumber of suppliers in many configurations. For example, Brain Products,in Munich, Germany makes and sells systems which capture EEG signals in64 channels. Their systems include a cap with 64 or more sensors andrecorders that have suitable software to receive the EEG signals fromthe sensors and simultaneously record them. Another EEG system supplieris EGI, with offices at 1600 Millrace Drive, Suite 307, Eugene, Oreg.97403. EGI makes the Geodesic brand of EEG systems. Their systems mayrecord 128-256 channels of EEG information.

The EEG signals are synchronized with the EMG signals using the Pen Downand Pen Up output signals of the digitizer 601. The digitizer signalsare input to EMG computer 141 and then output to EEG recording system651. As an alternative, the digitizer signals may be directly connectedto EEG recording system 651 or to the EEG computer 650. In operation,the Pen Down and Pen Up digitizer signals mark the beginning and the endof one event where the subject writes the letter “3” or an equivalentcharacter or characters including and not limited to numbers, letters,symbols or groups thereof. The character or groups of characters areselected to have definite starting and stopping points and are normallywritten with the pen in continuous contact with the digitizer.

EEG and EMG Systems

Based upon the successful results of experiments with the off-the-shelfEEG system, we determined it would be feasible to make a dedicatedsystem that included EEG and EMG sensors, hardware and software. FIG. 7Ashows a version of a data acquisition system that contains amicroprocessor-based EMG and EEG data acquisition systems 710, 720 thatsend EMG and EEG data streams wirelessly via antenna 740 to a hostcomputer 750 having an antenna 752. The host computer 750 is connectedto a digitizer 601 of handwriting traces via a USB connector.Microcontroller 730 is programmed to synchronize EEG and EMG datastreams. Such programming within the ordinary capabilities of oneskilled in the art. The EEG and EMG signals are time stamped and the PenDown event is sensed by a digitizer. Each trial begins with a Pen Downevent and ends with a Pen Up event. The Pen Down event is used tosynchronize the EEG and EMG channels. The host computer receives thesimultaneous EEG and EMG signals. After a user presses down a pen, a PenDown signal is included in the EEG and EMG files and is used todistinguish sequential trials for each other. A trial is a time period,during which an individual character is analyzed. In operation, the hostreceives the data streams from the EMG and EEG systems. If themicrocontroller does not directly synchronize the two steams, then thehost may use the signals from the digitizer 601 to time stamp both EMGand EEG data streams.

We used a commercially available apparatus known as a LogiPen devicewith a digitizer tablet to record handwriting traces. EMGs were sampledwith bipolar surface EMG electrodes (Kendall Arbo). Our multi-channelEMG laboratory system of FIG. 6 consisted of the amplifiers powered byone 9 V battery and National Instruments digitizer NI 9215 with thesample rate of 1000 Hz. Handwriting and EMG inputs were synchronized bya LabVIEW data acquisition program based on Pen Down events. EMGs ofseveral hand muscles were simultaneously recorded. In the systemconsisting of differential channels (FIG. 8), one pair of electrodes 810sampled EMG activity from flexor pollicis brevis and abductor pollicisbrevis. The second pair of electrodes 812 recorded from the first dorsalinteresseus, and the third from the second and third dorsal interosseusmuscles.

We used commercially available tablet such as the LogiManage band tabletas a digitizer of handwriting traces for producing X and Y coordinates,as well as a “press on paper” signal. EMG signals were recorded on adedicated computer 141, which was connected to an EEG recording system651 via a printer cable. The data from EEG recording system was saved infiles on EEG dedicated computer 650. These files also contained the“press on paper” signals and their time stamps that we used to determinethe trial intervals. Analog to Digital Converter (A/DC) 125 wasconnected to the EEG computer 141 via a USB port. A BNC adapter 130 isneeded because off the shelf Analog to Digital Converters usuallyrequire USB types of input connectors, but analog outputs are different.

Each of the computers has a processor, memory, a visual display, inputand output ports including USB ports and ports for sending and receivingdata wirelessly via such protocols as Bluetooth. The A/D convertersamples the real-time EMG or EEG signals at selected times and storesthe value of each sampled signals a biometric data signal. The time ofoccurrence of the biometric data signal is also stored with the data forlater analysis. The memory of the computer includes random access memoryand read only memory. Software and data are stored in the memory.Software includes operating system software and application software forprocessing data. In particular, the software includes one or moremodules with the ability to process the signal data in accordance withthe algorithms disclosed herein to generate the Pearson correlationcoefficients. One the data is processed, the operator of the computermay then use the graphical software to display stored subject and storedpatient biometric data signals on the computer display. As analternative, the computer may include further hardware or software orboth to automatically compare patient signal data to the subject storeddata and output a signal or other indicia that the biomarker for adisorder is likely present or not.

The patient uses a stylus or pen to write the same character orcharacters on a digitizing tablet 601. The tablet 610 is coupled to thecomputer 141. The tablet generates time-based The patient uses a stylusor pen to write the same character or characters on a digitizing tablet601. The tablet 610 is coupled to the computer 141. The tablet generatestime-based digital signals of X and Y coordinates of the patient'shandwriting. Those time-based signals are synchronized with the EMGsignals data by a synchronizing software module. Other software modulesprocess signal data representative of repeated identical handwritingsamples to generate templates of the average intensity of the EMGsignals over time and to generate Pearson correlation coefficientsbetween two or more streams of EEG signals. The results of typicalsoftware processing are shown in FIGS. 9-12.

Those time-based signals are synchronized with the EMG signals data by asynchronizing software module. Other software modules process signaldata representative of repeated identical handwriting samples togenerate templates of the average intensity of the EMG signals over timeand to generate Pearson correlation coefficients between two or morestreams of EEG signals. The results of typical software processing areshown in FIGS. 9-12.

The patient's head is covered with an EEG cap which generates 64channels of EEG signals. Those signals are also synchronized with thetime-based X-Y coordinate data signals for the digitizer. An EEG dataprocessing software module processes the EEG signals to calculate thePearson coefficient correlations during the handwriting samples andgenerate the correlation graphs shown in FIGS. 22-14-18 and 22-25.

A third embodiment of our invention as shown in FIG. 7B relies only onEMG signals. It is similar to the systems shown above in FIGS. 6 and 7except that the EEG sensors and recording equipment and apparatus areomitted. The embodiment of FIG. 7B uses the glove 301 to generate two ormore channels of EMG signals. The signals are amplified and converted byADC 125 into streams of biometric signals data corresponding to the EMGsignals generated during handwriting. The patient uses a stylus or pento write the same character or characters on a digitizing tablet 601.The tablet 610 is coupled to the computer 141. The tablet generatestime-based digital signals of X and Y coordinates of the patient'shandwriting. Those time-based signals are synchronized with the EMGsignals data by a synchronizing software module. Other software modulesprocess signal data representative of repeated identical handwritingsamples to generate templates of the average intensity of the EMGsignals over time and to generate Pearson correlation coefficientsbetween two or more streams of EEG signals. The results of typicalsoftware processing are shown in FIGS. 9-12.

Software and Algorithm Development

The purpose and function of data acquisition software is to acquiresensor data and synchronize the data from multiple sensors. Suchsynchronization may be achieved different ways depending upon thehardware devices, their operating systems and the host computeroperating system. Those skilled in the art understand that differenthardware may require different software, but the task of synchronizingis the same and may be achieved without inventive effort. The dataanalysis software relies upon data acquisition software that is writtenin LabVIEW and saved in MATLAB format. Another program also written inMATLAB format reads those files and analyzes them.

Application software in computer stores patient biometric signals asbiometric signal data in the computer memory. Software also processespatient biometric signal data and generates generating graphicrepresentations of the stored biometric signal data for the healthysubject and for the patient Further software presents on the visualdisplay graphic representations of the healthy subject and the patientto enable a viewer to compare characteristics of the data.

LabVIEW is the trade name for suite of software products supplied byNational Instruments. It provides a graphical programming environmentused by engineers and scientists to develop sophisticated measurement,test, and control systems using intuitive graphical icons and wires thatresemble a flowchart. It offers integration with thousands of hardwaredevices and provides hundreds of built-in libraries for advancedanalysis and data visualization—all for creating virtualinstrumentation. The LabVIEW platform is scalable across multipletargets and operating systems.

MATLAB is a trade name describing a suite of computer programs offeredby The MathWorks, Inc. of Natick, Mass. MATLAB® software provides ahigh-level technical computing language and interactive software foralgorithm development, data visualization, data analysis, and numericalcomputation. Using MATLAB, scientists and engineers can solve technicalcomputing problems faster than with traditional programming languages,such as C, C++, and Fortran. MATLAB may be used in a wide range ofapplications, including signal and image processing, communications,control design, test and measurement, financial modeling and analysis,and computational biology. A number of add-on toolboxes (collections ofspecial-purpose MATLAB functions) extend the MATLAB environment to solveparticular classes of problems in these application areas. MATLAB canintegrate its code with other languages and applications, and distributeMATLAB algorithms and applications.

The foregoing software tools were used for data acquisition, analysis,computation of values, such as Pearson coefficients, and display ofresults. The detailed algorithms described in this patent show oneskilled in the art how the data acquired by the data acquisitionsoftware is processed in by MATLAB or an equivalent program. As such, itis sufficient for those skilled in the art to have examples of dataacquisition software since they are expected to be familiar with usingLabVIEW and MATLAB programs or their equivalents and will be able toadapt such software to execute the algorithms disclosed herein.

Software synchronizes EEG and EMG signals with respect to each otheraccording to press down signal from the digitizer of handwriting traces.Handwriting trials are defined as the epochs starting 500 ms before thepen touches a paper and ending 1000 ms after this moment of time. Theamplitudes of EMG and EEG signals, A_(emg) and A_(eeg) is squared to getthe signal “intensity”, I_(emg)=A_(emg) ² and I_(eeg)=A_(eeg) ².

To study time-dependent statistical and correlation properties of EMGand EEG signals, 1500-ms trials are subdivided into 15 time intervals,each with the duration of 100 ms, and the signal “energy” is calculatedfor each of 15 intervals as the sum,

$\begin{matrix}{{E_{{emg}{({eeg})}}\left( {n,\alpha,j} \right)} = {\sum\limits_{interval}{I_{{emg}{({eeg})}}\left( {n,\alpha,j} \right)}}} & (1)\end{matrix}$where n=1÷15, a=1÷3, j=1÷N (where N is the total number of trials),enumerate the time intervals, recording channels, and trials,respectively. To obtain dimensionless variables for each interval, theenergies E_(emg(eeg))(n, α, j) are normalized by dividing by thecorresponding mean values, E^(mean) _(emg(eeg))(n, α)=

(E_(emg(eeg))(n, α, j)

. Here and hereafter

. . .

stands for averaging over trials,

$\begin{matrix}{\left\langle \mspace{14mu}\ldots\mspace{14mu} \right\rangle = {\frac{1}{N}{\sum\limits_{j = 1}^{j = N}\mspace{14mu}\ldots}}} & (2)\end{matrix}$Thus, in our analysis EMG and EEG signals for each recording channel arecharacterized by the logarithms of dimensionless energies

$\begin{matrix}{{ɛ_{{emg}{({eeg})}}\left( {n,\alpha,j} \right)} = \frac{E_{{emg}{({eeg})}}\left( {n,\alpha,j} \right)}{E_{{emg}{({eeg})}}^{mean}\left( {n,\alpha} \right)}} & (3)\end{matrix}$We have found [2] that trial-to-trial distribution of valuesε_(emg(eeg)) is well described by log-normal distribution [3].Therefore, we compute the logarithms of the dimensionless energies asfollows:L _(emg)(n,a,j)=log [ε_(emg)(n,α,j)] and L _(eeg)(n,α,j)=log[ε_(eeg)(n,α,j)]  (4)and Pearson correlation coefficients defined as

$\begin{matrix}{{P_{emg}\left( {n,m,\alpha,\beta} \right)} = \frac{\begin{matrix}\left\langle \left( {{L_{emg}\left( {n,\alpha,j} \right)} - \left\langle {L_{emg}\left( {n,\alpha,j} \right)} \right\rangle} \right) \right. \\\left. \left( {{L_{emg}\left( {m,\beta,j} \right)} - \left\langle {L_{emg}\left( {m,\beta,j} \right)} \right\rangle} \right) \right\rangle\end{matrix}}{{S_{emg}\left( {n,\alpha} \right)}{S_{emg}\left( {m,\beta} \right)}}} & \left( {5a} \right) \\{{P_{eeg}\left( {n,m,\alpha,\beta} \right)} = \frac{\begin{matrix}\left\langle \left( {{L_{eeg}\left( {n,\alpha,j} \right)} - \left\langle {L_{eeg}\left( {n,\alpha,j} \right)} \right\rangle} \right) \right. \\\left. \left( {{L_{eeg}\left( {m,\beta,j} \right)} - \left\langle {L_{eeg}\left( {m,\beta,j} \right)} \right\rangle} \right) \right\rangle\end{matrix}}{{S_{eeg}\left( {n,\alpha} \right)}{S_{eeg}\left( {m,\beta} \right)}}} & \left( {5b} \right)\end{matrix}$where S_(emg(eeg)) are the standard deviations of the functionsL_(emg)(n, α, j) and L_(eeg)(n, α, j), respectively. Since the valuesL_(emg(eeg)) are normally distributed over trials, one can compute notonly the correlation coefficients but also the confidence interval, or,in other words, the upper and lower limits of a correlation coefficientmagnitude, which contains the magnitude with the probability of 95% (orp-value=0.95). Owing to the definitions (5), the modulus of Pearsoncoefficients does not exceed 1.

Parkinson Disease Biomarkers

Statistical and correlation properties of recorded EMG and EEG signalswere studied by using the above-described algorithms. These studiesresulted in several visible biomarkers of Parkinson disease which welist and discuss below.

Deterioration of EMG Signals Templates

First, we computed so-called EMG templates, which are defined astrial-average intensity of EMG signals, I(n, α)=

(I_(emg)(n, α, j)

. In FIG. 9 we show a typical template computed for EMG signals in twodifferent channels of a control healthy subject. FIG. 10 represents atemplate computed for a PD patient. Both templates show the averageintensities computed for 40 trials of handwriting of digit “3”. Thepoint t=500 ms corresponds to the moment of time when a pen touches apaper the first time. It is readily seen that the template of controlsubject of FIG. 9 has well determined bursts corresponding tohandwriting itself, while EMG intensity in the vicinity where a subjectis only firmly holding a pen by the fingers is much lower. In the caseof results for a PD patient as shown in FIG. 10, the EMG intensity isalmost homogeneously distributed over the whole epoch, correspondingboth to firmly holding a pen by the fingers and to moving the hand andthe fingers to produce written text.

Changes of Pearson Correlation Coefficients Between Different EMGChannels

Next, we compare Pearson correlation coefficients of control and PDsubjects computed between two EMG channels. FIG. 11 shows time behaviorof the Pearson correlation coefficients for a healthy control subject.There the center bars represent a computed magnitude of the coefficient,while the left and right bars represent the upper and lower limits,respectively. Thus, with the probability 95% a real magnitude of thecoefficient lies between the magnitudes shown by the left and rightbars.

We observed quite a high correlation between EMG signals (EMG1 and EMG2)recorded from two groups of hand muscles, involved in handwriting, inall 15 time intervals. Here the interval 6 corresponds to the first 100ms after a pen touches a digital paper. For the PD patient we did notobserve a proper correlation between these two different hand-musclegroups. Correlation is practically absent in all time intervals aseasily seen in FIG. 12.

Growth of Cortex Areas Activated During Handwriting

EEG signals were recorded by a standard 64-channel EEG device 710.Location of channels is shown schematically in FIG. 13. As shown in FIG.13, the subject's nose 712 orients the cap 710 to place half the sensorson each side of the subject's head. The first 32 channels are shown bygrey circles, while the second 32 channels, from channel 33 to channel64, are shown by white circles, and their numbering is obtained byadding of 32 to the numbers in white circles.

First, we computed the correlation coefficients defined by Eq. (5b) butat the same time intervals, i.e. at n=m, P_(eeg)(n, n, α,β), wheren=1÷15 numerates the time intervals, while α=1÷64 and β=1÷64, numeratethe EEG channels according to schematics presented in FIG. 5.

FIG. 14 represents the correlation coefficients for channel 13 and allother channels, P_(eeg)(n, α=13, β), for all time intervals for acontrol healthy subject. It is easily seen that the time dependence isquite weak for all correlators. And analogous time behavior is observedfor both healthy subjects and PD patients. That allows us to average thecorrelation coefficients over time intervals

$\begin{matrix}{{P_{mean}\left( {\alpha,\beta} \right)} = {\frac{1}{M}{\sum\limits_{n = 1}^{M}{P\left( {n,\alpha,\beta} \right)}}}} & (6)\end{matrix}$where M=15 is the number of time intervals.

FIGS. 15-18 show time-averaged Pearson correlation coefficients P_(mean)for channels 13(C3) 722 a, 15(C4) 751 a, 23(P7) 724 a, and 27(P8) 723 a,respectively, with all other channels, computed for a healthy controlsubject. The channel numbers are vendor specific. In parenthesis are thenames of channels according to the international naming convention. Thechannels are defined by the boundary surrounding the indicated sensors.FIG. 15 shows the Pearson correlation coefficients P_(mean) of channel13 (C3) with all channels during handwriting (healthy control). FIG. 16shows Pearson correlation coefficients P_(mean) of channel 15 (C4) withall channels during handwriting (healthy control). FIG. 17 shows Pearsoncorrelation coefficients P_(mean) of channel 23 (P7) with all channelsduring handwriting (healthy control). FIG. 18 shows Pearson correlationcoefficients P_(mean) of channel 27 (P8) with all channels duringhandwriting (healthy control).

An EEG activity of healthy control subjects during handwriting islocalized in, at least, four relatively small regions around thechannels 13(C3) and 23(P7) on the left hemisphere and symmetricallyaround the channels 15(C4) and 27(P8) on the right hemisphere.Correlation coefficients are large for channels inside regions andsignificantly fall down for channels, which belong to different regions,as shown in FIG. 19, 20.

FIGS. 21-23 represent the correlation coefficients P_(mean) computed forhealthy control subject (FIG. 21) and PD patients (FIGS. 22, 23) atstage I and III, respectively, of disease. As one can see from FIG. 21,the healthy control does not have a lot of highly correlated channelsduring handwriting, because most of the correlation coefficients arerelatively low. However, FIG. 22 shows results of a patient in Stage IPD and FIG. 23 shows results for a patient in Stage III PD. Both figuresshow relatively highly and broad, non-localized correlations. Thus, anEEG activity of PD patients is delocalized and extended to practicallywhole cortex, because correlation coefficients between almost allchannels on both hemispheres are large and shown in red, as easily seenin FIGS. 22 and 23.

Growth of a Characteristic Correlation Time of EEG Signals

We also computed the correlation functions in equation (5b), P_(eeg) (n,m, α, β), for EEG signals depending on two sets of time intervals,n=1÷15 and m=1÷15. This allows one for estimating a characteristiccorrelation time for a single channel and for two different channels.

FIG. 24 represents the correlation coefficient for a single channel 13,P_(eeg) (n, m, α=13, β=13), of a healthy control subject as a functionof two time intervals. The correlation coefficient is naturally equal to1 along the diagonal n=m, and falls down when the time intervals aredifferent. It decreases from 1 to about 0.3 already for n=m±1. Thatmeans that a characteristic correlation time can be estimated as 100 msor shorter.

More precise measurements require more accurate synchronization ofbio-signal trials. That hardly can be done, because of neuronalvariability during handwriting [2]. For example, even time of pen motionis varied from trial to trial in quite wide limits of the order of50-100 ms. An analogous time behavior with the characteristiccorrelation time of the order of 100 ms (or shorter) we also observe forcorrelation coefficients computed for two different channels providedthey belong to one of above-discussed activity regions.

However, for PD patients we observed a significant increase of thecharacteristic correlation time, as can be easily seen from FIG. 25. AnEEG activity correlates even for large difference between timeintervals, |n−m|, especially for time intervals before the moment oftime when a pen touches a paper (intervals 1÷5) and during handwritingitself (intervals 6÷10).

REFERENCES

-   1. Linderman, M., Lebedev, M. A and Erlichman, J. S. Recognition of    handwriting from electromyography, PLoS One 4 (8) (2009).-   2. Rupasov, V. I., Lebedev, M. A., Erlichman, J. S., and    Linderman, M. Neuronal variability during handwriting: Lognormal    distribution, submitted for publication.-   3. Bendat, J. S. and Piersol, A. G. Measurements and analysis of    random data (John Wiley & Sons, New York, USA, 1968).

Alternate Configurations

A machine-readable medium includes any mechanism that provides (i.e.,stores and/or transmits) information in a form readable by a machine(e.g., a computer). For example, a machine-readable medium includes, butis not limited to, read only memory (ROM); random access memory (RAM);magnetic disk storage media; optical storage medial; flash memorydevices; electrical, optical, acoustical or other form of propagatedsignals (e.g., carrier waves, infrared signals, digital signals, etc.);storage media; radio channels; and wireless channels and various othermediums capable of storing, containing, or carrying instructions and/ordata.

It is possible to provide a variety of alternative configurations forimplementing the technique. By way of example, the glove, a sleeve, orsome other article of apparel can be adapted to fit on a part of thebody, other than the hand, which is capable of receiving nerve impulsesfor fine motor control. Examples include the feet and face. In thatmanner, the sensed EMG signals can be used for biometric assessment. Itis further possible to use such EMG signals for robotic manipulation orfor sending signals for computer processing. In that manner, finemanipulations may be possible without the use of the hands.

The previous description of some embodiments is provided to enable anyperson skilled in the art to make or use the present technique. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other embodiments without departing from the spirit or scopeof the present disclosure. For example, one or more elements can berearranged and/or combined, or additional elements may be added.Further, one or more of the embodiments can be implemented by hardware,software, firmware, middleware, microcode, or any combination thereof.Thus, the present disclosure is not intended to be limited to theembodiments shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

Having described the technique in detail and by reference to theembodiments thereof, it will be apparent that modifications andvariations are possible, including the addition of elements or therearrangement or combination or one or more elements, without departingfrom the scope of the disclosure which is defined in the appendedclaims.

The invention claimed is:
 1. A method of detecting a disorder of the central nervous system, comprising: collecting EMG signals corresponding to a defined handwriting activity over a plurality of trials; squaring one or more amplitudes of the collected EMG signals to obtain one or more signal intensities; subdividing the obtained signal intensities into a plurality of time intervals; calculating an energy value for each of the plurality of time intervals; normalizing the energy values for each of the plurality of time intervals; computing a log-normal trial-to-trial distribution of the normalized energy values in each time interval; computing correlation functions between two time intervals of the plurality of time intervals; analyzing the correlation functions to distinguish healthy controls from patients with neurological disorders.
 2. A method of detecting a disorder of the central nervous system, comprising: collecting EEG signals corresponding to a defined handwriting activity over a plurality of trials; squaring one or more amplitudes of the collected EEG signals to obtain one or more signal intensities; subdividing the obtained signal intensities into a plurality of time intervals; calculating an energy value for each of the plurality of time intervals; normalizing the energy values for each of the plurality of time intervals; computing a log-normal trial-to-trial distribution of the normalized energy values in each time interval; computing correlation functions between two time intervals of the plurality of time intervals; analyzing the correlation functions to distinguish healthy controls from patients with neurological disorders.
 3. A method of detecting a disorder of the central nervous system, comprising: simultaneously collecting EMG signals and EEG signals corresponding to a defined handwriting activity over a plurality of trials; squaring one or more amplitudes of the collected EMG signals and the collected EEG signals to obtain one or more signal intensities; subdividing the obtained signal intensities into a plurality of time intervals; calculating an energy value for each of the plurality of time intervals; normalizing the energy values for each of the plurality of time intervals; computing a log-normal trial-to-trial distribution of the normalized energy values in each time interval; computing correlation functions between two time intervals of the plurality of time intervals; analyzing the correlation functions to distinguish healthy controls from patients with neurological disorders. 